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Wireless Sensor Network Localization Based on Cuckoo Search Algorithm

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Abstract

Many applications of wireless sensor networks (WSN) require information about the geographical location of each sensor node. Devices that form WSN are expected to be remotely deployed in large numbers in a sensing field to perform sensing and acting task. The goal of localization is to assign geographical coordinates to each device with unknown position in the deployment area. Recently, the popular strategy is to apply optimization algorithms to solve the localization problem. In this paper, the cuckoo search algorithm is implemented to estimate the sensor’s position. The proposed approach has been compared in terms of localization error with particle swarm optimization (PSO) and various variants of biogeography based optimization (BBO). The results show that our method outperforms the PSO and BBO variants which are recently used in the literature.

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References

  1. Pal, A. (2010). Localization algorithms in wireless sensor networks: Current approaches and future challenges. Network Protocols and Algorithms, Vol. 2, No. 1. ISSN 1943–3581.

  2. Kannan, A. A., Mao, G., & Vucetic, B. (2006). Simulated annealing based wireless sensor network localization. Journals of Computer, 1(2), 15–22.

    Google Scholar 

  3. Doherty, L. (2001). Convex position estimation in wireless sensor networks. In: Twentieth annual joint conference of the IEEE INFOCOM computer and communications societies. Proceedings (Vol. 3, pp. 1655–1663).

  4. Pottie, G. J., & Kaiser, W. J. (2000). Wireless integrated sensor networks. Communications of ACM, 43(5), 51–58.

    Article  Google Scholar 

  5. Akyildiz, I., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer networks, 38(4), 393–422.

    Article  Google Scholar 

  6. Wang, J., Ghosh, R. K., & Das, S. K. (2010). A survey on sensor localization. Journal of Control Theory and Applications, 8(1), 2–11.

    Article  MATH  Google Scholar 

  7. Boukerche, A., Oliveira, H., Nakamura, E., & Loureiro, A. (2007). Localization systems for wireless sensor networks. IEEE Wireless Communications, 14(6), 6–12.

    Article  Google Scholar 

  8. Hightower, J., & Borriello, G. (2001). Location systems for ubiquitous computing. Computer, 34(8), 57–66.

    Article  Google Scholar 

  9. Niculescu, D., & Nath, B. (2001). Ad hoc positioning system (aps). In Global telecommunications conference. GLOBECOM IEEE, (Vol. 5, pp. 2926–2931).

  10. Bulusu, N., Estrin, D., Girod, L, & Heidemann, J. (2001). Scalable coordination for wireless sensor networks: Self-configuring localization systems. In International symposiumon communication theory and applications (ISCTA2001). Ambleside, UK.

  11. Savvides, A., Park, H., & Srivastava, M. (2002). The bits and flops of the n-hop multilateration primitive for node localization problems. In Proceedings of the 1st ACM international workshop on wireless sensor networks and applications (pp. 112–121). ACM.

  12. Di Rocco M., & Pascucci, F. (2007). Sensor network localization using distributed extended kalman filter. In IEEE/ASME international conference on advanced intelligent mechatronics (pp. 1–6).

  13. Kalman, R. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(Series D), 35–45.

    Article  Google Scholar 

  14. Shang, Y., & Ruml, W. (2004). Improved MDS-based localization. In Twenty third annual joint conference of the IEEE computer and communications societies INFOCOM (Vol. 4, pp. 2640–2651).

  15. Biswas, P., Lian, T., Wang, T., & Ye, Y. (2006). Semi definite programming based algorithms for sensor network localization. ACM Transactions on Sensor Networks (TOSN), 2(2), 188–220.

    Article  Google Scholar 

  16. Yun, S., Lee, J., Chung, W., Kim, E., & Kim, S. (2009). A soft computing approach to localization in wireless sensor networks. Expert Systems with Applications, 36(4), 7552–7561.

    Article  Google Scholar 

  17. Zhang, Q., Wang, J., Jin, C., & Zeng, Q. (2008). Localization algorithm for wireless sensor network based on genetic simulated annealing algorithm. In 4th international conference on eireless communications networking and mobile, computing, WiCOM08 (pp. 1–5).

  18. Zhang, Q., Huang, J., Wang, J., Jin, C., Ye, J., & Zhang, W. (2008). A new centralized localization algorithm for wireless sensor network. In Third international conference on communications and networking in China, physical world with pervasive networks, pervasive Computing, IEEE, China Com 2008 (pp. 625–629).

  19. Li, Y., Xing, J., Yang, Q., & Shi, H. (2009). Localization research based on improved simulated annealing algorithm in WSN. In 5th international communications of conference on wireless communications, networking and mobile computing. WiCom 09 (pp. 1–4). IEEE

  20. Kulkarni, R., Venayagamoorthy, G., & Cheng, M. (2009). Bio-inspired node localization in wireless sensor networks. In IEEE international conference on systems, man and cybernetics, SMC (pp. 205–210).

  21. Gopakumar A., & Jacob, L. (2008). Localization in wireless sensor networks using particle swarm optimization. In IET international conference on wireless, mobile and multimedia networks (pp. 227–230).

  22. Stoleru R., & Stankovic, J. A. (2004). Probability grid: A location estimation scheme for wireless sensor networks. In First annual IEEE communications society conference on sensor and ad hoc communications networks, IEEE SECON (pp. 430–438).

  23. Chuang, P., & Wu, C. (2008). An effective pso-based node localization scheme for wireless sensor networks. In Ninth international conference on parallel and distributed computing, applications and technologies, PDCAT (pp. 187–194). IEEE.

  24. Vecchio, M., Valcarce, R. L., & Marcelloni, F. (2012). A two-objective evolutionary approach based on topological constraints for node localization in wireless sensor networks. Applied Soft Computing (pp. 1891–1901). Elsevier

  25. Yang, X., & Deb, S. (2009). Cuckoo search via Lévy flights. In World congress on nature & biologically inspired computing (NaBIC2009), IEEE, 978-1-4244-5612-3/09.

  26. Abdul Rani, K. N., Abd Malek, M. F., & Siew-Chin, N. (2012). Nature-ispired cuckoo search algorithm for side lobe suppression in a symmetric linear antenna array. In Radio, Engineering, Vol. 21, No. 3.

  27. Brown, C., Liebovitch, L. S., & Glendon, R. (2007). Lévy flights in DobeJu/’hoansi foraging patterns. Human Ecology, 35, 129–138.

    Article  Google Scholar 

  28. Reynolds, A. M., & Frye, M. A. (2007). Free-flight odor tracking in Drosophila is consistent with an optimal intermittent scale-free search. PLoS One, 2, e354.

    Article  Google Scholar 

  29. Pavlyukevich, I. (2007). Lévy flights, non-local search and simulated annealing. Journal of Computational Physics, 226, 1830–1844.

    Article  MathSciNet  MATH  Google Scholar 

  30. Pavlyukevich, I. (2007). Cooling down Lévy flights. Journal of Physics A, Mathematical and Theoretical, 40, 12299–12313.

    Article  MathSciNet  MATH  Google Scholar 

  31. Shlesinger, M. F., Zaslavsky, G. M., & Frisch, U. (Eds.). (1995). Lévy flights and related topics in phyics. Berlin: Springer.

    Google Scholar 

  32. Shlesinger, M. F. (2006). Search research. Nature, 443, 281–282.

    Article  Google Scholar 

  33. Payne, R. B., Sorenson, M. D., & Klitz, K. (2005). The Cuckoos. Oxford: Oxford University Press.

    Google Scholar 

  34. Patwari, N., Ash, J. N., Kyperountas, S., Hero, A. O., Moses, R. L., & Correal, N. S. (2005). Locating the nodes: Cooperative localization in wireless sensor networks. IEEE Signal Processing Magazine, 22(4), 54–69.

    Article  Google Scholar 

  35. Singh, S., Shivangna, S., & Mittal, E. (2013). Range based wireless sensor node localization using PSO and BBO and its variants. In International conference on communication systems and network technologies, IEEE, 978–0-7695-4958-3/13. doi:10.1109/CSNT.2013.72.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, University College of Engineering, Punjabi University, Patiala , 147002, PB, India

    Sonia Goyal & Manjeet Singh Patterh

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  1. Sonia Goyal
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  2. Manjeet Singh Patterh
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Correspondence to Sonia Goyal.

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Goyal, S., Patterh, M.S. Wireless Sensor Network Localization Based on Cuckoo Search Algorithm. Wireless Pers Commun 79, 223–234 (2014). https://doi.org/10.1007/s11277-014-1850-8

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